Non-Bayesian approaches use entropy [1]} or softmax scores as a measure of uncertainty, which only considers aleatoric uncertainty [2]}.
OOD detection in text classification using GRU [3]} or LSTM [4]} has been studied in [5]}, [6]}.
[7]} empirically study pre-trained transformers' performance on OOD detection. They point out transformers cannot clearly separate in-distribution (ID) and OOD examples.
In addition, OOD detection has also been studied in dialogue systems [8]} and document classification [9]}, [10]}.
Another line of non-Bayesian methods involves the calibration of probabilities.
Temperature scaling [11]} calibrates softmax probabilities by adding a scalar parameter to each class in a post-processing step.
[12]} explore the improvement of calibration and predictive uncertainty of models trained with mix-up [13]} in the NLP domain.
[14]} use pseudo samples on and off the data manifold for calibration.
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